from sklearn.datasets import load_iris
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
import numpy as np
import matplotlib.pyplot as plt
 
iris = load_iris()
X = iris.data[:, [2,3]]
y = iris.target
 
clf1 = SVC(kernel='poly')
clf1.fit(X,y)
 
clf2 = LogisticRegression()
clf2.fit(X,y)
 
 
def plot_estimator(estimator, X, y):
    x_min, x_max = X[:,0].min() - 1, X[:,0].max() + 1
    y_min, y_max = X[:,1].min() - 1, X[:,1].max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),
                         np.arange(y_min, y_max, 0.1))
    Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    plt.plot()
    plt.contourf(xx, yy, Z, alpha=0.4, cmap=plt.cm.RdYlBu)
    plt.scatter(X[:,0], X[:,1],c=y, cmap=plt.cm.brg)
    plt.xlabel('Petal.Length')
    plt.ylabel('Petal.Width')
    plt.show()
 
plot_estimator(clf2, X, y)
